This variedness of object definitions is challenging for self-supervised or unsupervised semantic segmentation as human annotations that carry the object definitions are, at most, used at test time.
However, the ability to learn self-supervised dense representations is desirable as this would allow scaling beyond object-centric images and allow effective learning on billions more generic images.
Furthermore, unsupervised segmentation can be highly useful as a starting point for more efficient data labeling, as segmentation annotations are even more expensive than image labelling [1]}.
To tackle the lack of a principled object definition during training, many methods resort to defining object priors such as saliency and contour detectors to induce a notion of objectness into their pretext tasks [2]}, [3]}, [4]}, [5]}, [6]}, effectively rendering such methods semi-supervised and potentially not generalizeable.
In this paper, we instead stay in the fully unsupervised domain and explore a novel, yet simple alternative for training densely.
We learn object parts (Leopart) through a dense image patch clustering pretext task.
Object part learning promises a principled formulation for self-supervised dense representation learning as object parts can be composed to form objects as defined in each benchmark, after generic pretraining.
